Multidimensional membership functions in T–S fuzzy models for modelling and identification of nonlinear multivariable systems using genetic algorithms

Adánez García-Villaraco, José Miguel and Al-Hadithi, Basil M. and Jiménez Avello, Agustín (2019). Multidimensional membership functions in T–S fuzzy models for modelling and identification of nonlinear multivariable systems using genetic algorithms. "Applied Soft Computing", v. 75 ; pp. 607-615. ISSN 1568-4946. https://doi.org/10.1016/j.asoc.2018.11.034.

Description

Title: Multidimensional membership functions in T–S fuzzy models for modelling and identification of nonlinear multivariable systems using genetic algorithms
Author/s:
  • Adánez García-Villaraco, José Miguel
  • Al-Hadithi, Basil M.
  • Jiménez Avello, Agustín
Item Type: Article
Título de Revista/Publicación: Applied Soft Computing
Date: February 2019
Volume: 75
Subjects:
Freetext Keywords: Fuzzy rules; Takagi–Sugeno model; Genetic algorithm; Multidimensional membership functions
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

(ENG) In this work, a new method for Takagi–Sugeno (T–S) fuzzy modelling based on multidimensional membership functions (MDMFs) is proposed. It is verified that the fuzzy inference method of one-dimensional membership functions (1DMFs) may place the fuzzy rules in inappropriate locations for modelling of nonlinear multivariable systems, while the application of MDMFs allows a better identification through a smaller number of fuzzy rules. The proposed method uses a genetic algorithm (GA) for the adjustment of the MDMFs and the T–S method for modelling and identification of the nonlinear system. As a validation example, a non linear multivariable system, a coupled tanks system, is chosen.The results show that the proposed method presents less identification error than the T–S method, with less number of fuzzy rules. (SPA) En este artículo de investigación se propone un método nuevo para el modelado borroso basado en funciones de pertenencia multidimensionales. Se emplea un algoritmo genético para el ajuste de las funciones de pertenencia multidimensionales y el método de Takagi-Sugeno para el modelado y la identificación del sistema no lineal. Se muestra que, comparado con el método tradicional de la inferencia borrosa de funciones de pertenencia monodimensionales, el método propuesto permite una mejor identificación mediante un menor número de reglas borrosas.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2014-53525-C3-1-RNAVEGASEUnspecifiedNavegación asistida mediante lenguaje natural = Assisted Navigation through Natural Language

More information

Item ID: 55412
DC Identifier: http://oa.upm.es/55412/
OAI Identifier: oai:oa.upm.es:55412
DOI: 10.1016/j.asoc.2018.11.034
Official URL: https://www.sciencedirect.com/science/article/pii/S1568494618306665?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 13 Jun 2019 07:02
Last Modified: 13 Jun 2019 10:26
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